Evaluation of the potential of full-waveform airborne hyperspectral lidar for forest chlorophyll content detection based on a three-dimensional radiative transfer model

Forest chlorophyll content (Cab) is a key indicator of vegetation photosynthetic capacity and physiological status, while its vertical distribution reflects canopy adaptation to light gradients. Passive optical remote sensing provides limited within-canopy information, and conventional LiDAR lacks biochemical sensitivity. To evaluate the potential of future full-waveform airborne hyperspectral LiDAR (FW-AHSL) for vertically resolved Cab detection, this study used the LESS three-dimensional radiative transfer model to simulate 32-band FW-AHSL waveforms in natural, regular, semi-random, random, and density-gradient forest scenes. Layer-specific vegetation indices, linear retrieval, density sensitivity analysis, and random forest mean decrease accuracy (MDA) were used to assess performance. Results showed that FW-AHSL distinguished echoes from different canopy layers and the ground. Multiple green, red-edge, and near-infrared indices correlated strongly with layer-specific Cab, with 11 indices showing mean correlations > 0.8 and 9 > 0.85. Semi-random and random scenes confirmed the potential for layer-specific Cab retrieval, although accuracy decreased from upper to lower layers. Density-gradient experiments showed stable upper-canopy retrieval but reduced middle- and lower-canopy performance due to occlusion and increasing density. MDA further indicated layer-dependent dominant indices, suggesting that differentiated waveform–spectral features are required. Overall, FW-AHSL shows strong potential for detecting vertical canopy-scale Cab in forests and supports future sensor design.